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Secure and Efficient Key Management for Deduplicated Cloud Storage systems

Secure and Efficient Key Management for Deduplicated Cloud S...

引用

Security of Information and Networks (SIN), International Conference on

作者： Jay Dave Anshul Kanodia Raj Srivastava Kushagra Singh Prithvi Hegde Hitaishi Desai Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Hyderabad India

Deduplication is a ubiquitous compression technique for cloud storage servers. It reduces storage and bandwidth requirements by avoiding duplicate copies of data. However, developing an encryption scheme for deduplication is a critical challenge because traditional encryption techniques are not compatible with deduplication. The existing state-of-the-art encryption schemes are at risk of brute-force attacks as they use deterministic techniques for key generation. Moreover, the user in the recent schemes suffers from storage overhead of deduplication metadata and encryption keys. In this paper, we introduce a secure and efficient key management scheme for deduplication. In our scheme, the user divides the file into blocks and encrypts each block using a random key. As a result, our scheme protects against brute-force attacks. In addition, the user securely shares the encryption keys on public storage servers with the aid of a private server. As a result, the proposed approach causes lower storage overhead. We implement our scheme in a real cloud scenario and evaluate the performance of our approach in terms of storage and computation overhead.

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Ensuring Electronic Health Record (EHR) Privacy using Zero Knowledge Proofs (ZKP) and Secure Encryption Schemes on Blockchain

Ensuring Electronic Health Record (EHR) Privacy using Zero K...

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Advancements in Computing (ICAC), International Conference on

作者： Ranaweera T.A.V.Y Hewage H.N.H Hapuhinna H.K.D.W.M.C.B. Preethilal K.L.K.T Amila Senarathne Laneesha Ruggahakotuwa Dept. Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

In the haste of EHR digitization, the protection of patient data becomes paramount, casting a spotlight on small healthcare clinics grappling with the threats of an ever more interconnected healthcare sector. That is due to the existing centralized system and traditional password-based authentications which cause a single point of failure. Besides this patient consent for EHR sharing is narrowed to a one-time consent. To overcome these problems, this study suggests a robust solution that makes data sharing utilizing blockchain while achieving privacy using web-based Zero Knowledge Proofs (ZKP) authentication and Homomorphic Encryption (HE). The research also considers securely outsourcing EHRs to research organizations to improve data exchange for research purposes. Along with patient-controlled encryption, consent control, and time-limited data access, it also explores dynamic consent management while complying with healthcare standards. Lastly, this study stream-lines secure data sharing with insurance providers while ensuring patient privacy and defending against insurance fraud.

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Secure Deduplication with Dynamic Key Management in Fog Enabled Internet of Things

Secure Deduplication with Dynamic Key Management in Fog Enab...

引用

IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Jay Dave Nikumani Choudhury Utkarsh Tiwari Samyu Kamtam Kudapa Sai Rohith Dept. of Computer Science & Information Systems Birla Institute of Technology & Science Pilani Hyderabad India

Fog computing is an extension of cloud computing and presents additional devices called fog devices near IoT devices providing services on behalf of cloud servers. Although fog computing brings several advantages, rapid growth in the data generated by IoT devices increases communication and computational costs. As data sensed by IoT devices may correlate, there is a high possibility of duplicate copies in the sensed data. Data deduplication is a compression technique that reduces communication and storage overhead by skipping the upload and storage of duplicate copies of data. However, deduplication for a fog-enabled IoT system introduces new security issues. In this paper, we propose a novel secure deduplication approach with dynamic key management in a fog-enabled IoT system. We introduce a multilayer encryption scheme with dynamic key management to prevent the access of revoked users to the data. We implement the proposed scheme in a realistic scenario using Raspberry Pi and Firebase cloud services. The performance analysis shows that our approach achieves confidentiality and forward secrecy along with lower storage, computational, and communication costs.

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Knowledge Graph Generation for Unstructured Data Using Data Processing Pipeline

Knowledge Graph Generation for Unstructured Data Using Data ...

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IEEE Annual International computer Software and Applications Conference (COMPSAC)

作者： Sushmi Thushara Sukumar Chung-Horng Lung Marzia Zaman Dept. of Systems and Computer Eng. Carleton University R&D Cistel Technology

The proliferation of technologies and unstructured data on the internet poses a persistent challenge in extracting valuable information from diverse formats. To address this, research leverages Machine Learning (ML) and Natural Language Processing (NLP) techniques. This study contributes to information extraction from unstructured text using a state-of-the-art pipeline, incorporating modules for coreference resolution (Neuralcoref), named entity linking (Wikifier API), and Relationship Extraction (RE) (OpenNRE and REBEL models). The resulting Knowledge Graph (KG) in Neo4j captures entity relationships. Experiments on a BBC news dataset analyzed the pipeline’s performance, focusing on RE. Accuracies of 61.4% (OpenNRE) and 87% (REBEL) were achieved. The research demonstrates the efficacy of the proposed pipeline in extracting structured knowledge from unstructured data, facilitating the preservation and utilization of valuable information.

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Auto Disturbance Rejection Control Based on A Neural Network Predictive State Observer

Auto Disturbance Rejection Control Based on A Neural Network...

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International Conference on Intelligent Human-Machine systems and Cybernetics, IHMSC

作者： Shengzhi Du BJ van Wyk NFS Nahri Chunling Tu Dept. Electrical Engineering Tshwane University of Technology Pretoria South Africa Dept. Computer Systems Engineering Tshwane University of Technology Pretoria South Africa

This paper addresses the auto disturbance rejection control (ADRC) for systems with time delay. ADRC is an effective control for most linear or nonlinear systems with noises and disturbances, because of its advantage on disturbance estimation and compensation. The extended state observer (ESO) is a critical component of ADRC. However, this observer is usually designed for systems without time delay. The time delay in a system could lead the ADRC unstable when the phase delay of the response cannot be ignored. This paper proposes a neural network based predictive state observer (NNPSO) to predict the response of systems with time delay. The simulation results validate the proposed method when systems with time delay are controlled.

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Automated Boundary Identification for Machine Learning Classifiers

Automated Boundary Identification for Machine Learning Class...

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Search-Based and Fuzz Testing (SBFT), IEEE/ACM International Workshop on

作者： Felix Dobslaw Robert Feldt Dept. of Quality Mngmt Communication and Inf. Systems Mid Sweden University Östersund Sweden Dept. of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

ISBN: (数字)9798400705625

ISBN: (纸本)9798350376012

AI and Machine Learning (ML) models are increasingly used as (critical) components in software systems, even safety-critical ones. This puts new demands on the degree to which we need to test them and requires new and expanded testing methods. Recent boundary-value identification methods have been developed and shown to automatically find boundary candidates for traditional, non-ML software: pairs of nearby inputs that result in (highly) differing outputs. These can be shown to developers and testers, who can judge if the boundary is where it is supposed to ***, we explore how this method can identify decision boundaries of ML classification models. The resulting ML Boundary Spanning Algorithm (ML-BSA) is a search-based method extending previous work in two main ways. We empirically evaluate ML-BSA on seven ML datasets and show that it better spans and thus better identifies the entire classification boundary(ies). The diversity objective helps spread out the boundary pairs more broadly and evenly. This, we argue, can help testers and developers better judge where a classification boundary actually is, compare to expectations, and then focus further testing, validation, and even further training and model refinement on parts of the boundary where behaviour is not ideal.

关键词： Training Conferences Machine learning Fuzzing Software systems Classification algorithms Testing

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Does Sri Lanka Have a Good Maritime Strategy to Stop Cyber-Terrorist Attacks

Does Sri Lanka Have a Good Maritime Strategy to Stop Cyber-T...

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International Conference on Computing and Networking technology (ICCNT)

作者： Samaraweera S. A. R. P Siriwardana S. M. D. N W. D. V. Fernando D. N. Rajakarunaratne Dept. of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

Maritime cyber-terrorist attacks have become a major concern to the entire world in recent decades. Everyone should be more aware of marine strategies to prevent cyberterrorist attacks, both locally and globally. This study investigates how Sri Lanka and the Sri Lanka Navy (SLN) may successfully address maritime security concerns by utilizing current resources and developing a marine strategy to counter such maritime cyber-terrorism assaults. The literature review in this thesis primarily evaluates the level of understanding in Sri Lanka concerning when marine cyber terrorism strikes may occur. Furthermore, this thesis investigates if Sri Lanka has any maritime rules and regulations. The use of such laws and regulations will concentrate on how they can increase their expertise.

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An Optimal Optimization of Software Development Cost Estimation Using Genetic Algorithm

An Optimal Optimization of Software Development Cost Estimat...

引用

Sciences and Techniques of Automatic Control and computer Engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Mabroukah Amarif Salha Owaydat dept. of Information Systems Faculty of Information Technology Sebha University of Libya Sebha Libya dept. of Computer Sciences Faculty of Sciences Sebha University of Libya Sebha Libya

ISBN: (数字)9798350372632

ISBN: (纸本)9798350372649

Estimating effort allows managers and software engineers to accurately anticipate, forecast, and quote schedule, budget, and manpower requirements. Determining estimated project cost, duration, and maintenance efforts well in advance Of the development stages is the biggest defiance to be attained for software projects. Formal models for cost estimation, such as the Constructive Cost Model (COCOMO) are limited by their inability to manage uncertainty in software projects early development cycle. This research presents an optimal optimization of software development cost estimation using genetic algorithm. It provides a fine solution to set the uncertain and ambiguous properties of software factors. It adopts COCOMO II model formulas and manages fine-tuning parameters for accurate effort and scheduled time software cost estimation. An experiment has been carried out using a NASA data set in order to improve the proposed algorithm. The experimental results show a significant optimization for both software effort up to 97.27% accuracy and scheduled time up to 98.88%. This research has emphasized that applying the genetic algorithm with fine-tuning parameters of COCOMO II model definitely improve the software development cost estimation.

关键词： Costs Accuracy Uncertainty Computational modeling Software algorithms Estimation Software

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A CNN and Image-Based Approach for Malware Analysis

A CNN and Image-Based Approach for Malware Analysis

引用

2022 International Conference on Emerging Trends in Computing and Engineering Applications, ETCEA 2022

作者： Migdady, Aya Smadi, Lara Yaseen, Qussai Jordan University of Science and Technology Dept. of Computer Information Systems Irbid22110 Jordan Ajman University CEIT College AIRC Center United Arab Emirates

ISBN: (纸本)9781665477093

Malware attacks have various types, patterns, and volumes and have become more sophisticated and severe. Using machine learning to classify and detect malware is one of the approaches to mitigate malware attacks. However, malware classification suffers from some challenges such as the time required in manipulating a huge number of malware files. This paper proposes a Convolutional Neural Network (CNN) model and a pre-processing approach to solve the aforementioned issue. The contribution of this paper is based on converting the dataset into RGB images followed by scale maximization step. In addition, the paper proposes a preprocessing approach for the input datum of images. The results prove that the proposed preprocessing methods have a strong impact on enhancing the overall accuracy by increasing the accuracy from 92.5% to 98%. © 2022 IEEE.

关键词： Malware

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VascularVision: Mixed Reality Based Teaching and Learning Environment for Cardiovascular Conditions

VascularVision: Mixed Reality Based Teaching and Learning En...

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Advancements in Computing (ICAC), International Conference on

作者： Hashith Nissanka Sajini Wijayasundara Jude Rajarathne Sahan Vidanagamage Didula Thanaweera Arachchi Pradeep K. W. Abeygunawardhana Dept. Information Technology Sri Lanka Insitute of Information Technology Kandy Sri Lanka Dept. Information Technology Sri Lanka Insitute of Information Technology Colombo Sri Lanka Dept. Information Technology Sri Lanka Insitute of Information Technology Malabe Sri Lanka Dept. Computer Systems Engineering Sri Lanka Insitute of Information Technology Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

Developments in Augmented Reality (AR) and Virtual Reality (VR) have the potential to revolutionize medical education, especially in the areas of Coronary Angiography and Cardiac Auscultation. By giving medical and nursing students realistic, interactive experiences, the suggested VR and AR settings seek to modernize medical education by addressing the issues regarding the lack of real-world practice. Interactive 3D models, such as a realistic virtual body, rotatable heart models, ECG recordings, and real-life heart sounds, are available in the VR Cardiac Auscultation Environment. With the use of these components, students can practice diagnosing diseases including bradycardia, tachycardia, and cardiac murmurs in a dynamic and risk-free setting, improving their diagnostic skills. Similar to this, the AR Cardiac Auscultation Application combines interactive quizzes, machine learning for diagnostic verification, and real-time ECG scans to provide a powerful tool for teaching students how to recognize and understand various heart diseases and noises, this also can be a verification tool for junior doctors. By giving students firsthand experience with intricate medical procedures, virtual reality and augmented reality platforms offer a compelling and realistic approach to medical education. According to the research, these instruments can greatly enhance diagnostic proficiency, which will ultimately result in better educational outcomes and more qualified healthcare workers.

关键词： Heart Solid modeling Three-dimensional displays Tachycardia Education Medical services Electrocardiography Medical diagnostic imaging Augmented reality Diseases

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